Real world problems, such as massive biological data analysis and knowledge discovery, adaptive speech recognition and life-long language acquisition, adaptive intelligent prediction and control systems, intelligent agent-based systems and adaptive agents on the Web, mobile robots, visual monitoring systems, multi-modal information processing, intelligent adaptive decision support systems, adaptive domestic appliances and intelligent buildings, systems that learn and control brain and body states from a biofeedback, systems which classify bio-informatic data, and other systems require sophisticated solutions for building on-line adaptive knowledge base systems.
Such systems should be able to learn quickly from a large amount of data, adapt incrementally in an on-line mode, have an open structure so as to allow dynamic creation of new modules, memorise information that can be used later, interact continuously with the environment in a “life-long” learning mode, deal with knowledge as well as with data, and adequately represent space and time in their structure.
Well established neural network and artificial intelligence (AI) techniques have difficulties when applied for on-line knowledge based learning. For example, multi-layer perceptrons (MLP) and backpropagation learning algorithms have a number of problems, for example catastrophic forgetting, local minima problem, difficulties in extracting rules, inability to adapt to new data without retraining on old data, and excessive training times when applied to large data sets.
The self-organising map (SOM) may not be efficient when applied for unsupervised adaptive learning on new data, as the SOM assumes a fixed structure and a fixed grid of nodes connected in a topological output space that may not be appropriate to project a particular data set. Radial basis neural networks require clustering to be performed first and then the back propagation algorithm applied. Neuro-fuzzy systems cannot update the learned rules through continuous training on additional data without catastrophic forgetting.
These type of networks are not efficient for adaptive, on-line learning, although they do provide an improvement over prior techniques.